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Corrigendum to "Assessing the effectiveness of artificial neural networks (ANN) and multiple linear regressions (MLR) in forecasting AQI and PM10 and evaluating health impacts through AirQ+ (case study: Tehran)" [Environ. Pollut., 338 (2023) 122623].
Shams, Seyedeh Reyhaneh; Kalantary, Saba; Jahani, Ali; Parsa Shams, Seyed Mohammad; Kalantari, Behrang; Singh, Deveshwar; Moeinnadini, Mazaher; Choi, Yunsoo.
Afiliação
  • Shams SR; Department of Earth and Atmospheric Sciences, University of Houston, TX, 77204, USA.
  • Kalantary S; Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, 1416634793, Iran.
  • Jahani A; Research Center of Environment and Sustainable Development (RCESD), Tehran, Tehran, 141551156, Iran.
  • Parsa Shams SM; Department of Mechanical Engineering, College of Technical and Engineerin, Central Tehran University, Tehran, 1148963537, Iran.
  • Kalantari B; Department of Geography and Urban Planning, Shahid Beheshti University, Tehran, 1983969411, Iran.
  • Singh D; Department of Earth and Atmospheric Sciences, University of Houston, TX, 77204, USA.
  • Moeinnadini M; Department of Environment, Faculty of Natural Resources, Tehran University, Karaj, 1417935840, Iran.
  • Choi Y; Department of Earth and Atmospheric Sciences, University of Houston, TX, 77204, USA. Electronic address: ychoi23@central.uh.edu.
Environ Pollut ; 342: 123102, 2024 Feb 01.
Article em En | MEDLINE | ID: mdl-38086164
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Environ Pollut Ano de publicação: 2024 Tipo de documento: Article
Texto completo
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XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Environ Pollut Ano de publicação: 2024 Tipo de documento: Article